A Novel Bearing Fault Classification Method Based on XGBoost: The Fusion of Deep Learning-Based Features and Empirical Features

The key to intelligent fault diagnosis is to find relevant characteristics with the capability of representing different types of faults. However, the engineering problem is that a few simple empirical features (EFs) cannot obtain high classification accuracy, and complex feature engineering requires strong professional knowledge, which leads to limited applications on a general scale. In addition, intelligent feature extraction and classification methods without prior knowledge cannot guarantee that the model learned the general features used for classification, and its robustness and generalization are not strong when the objects with low-quality training data. Therefore, a fusion method of combining EFs and adaptive features extracted by a deep neural network is proposed. In this method, simple EFs that only need a few professional knowledge are adopted to realize general feature extraction and, hence, maintain the robustness of the model. A modified neural network structure (LiftingNet) is proposed to achieve the adaptive extraction of hidden features for specific objects, for realizing the high precision of bearing fault classification. In order to realize the fusing of EFs and adaptive features, XGBoost is utilized as the final classifier instead of common softmax. The feasibility and validity of the proposed method are verified by two data sets collected from motor bearings at stable work conditions. The experimental results show that the classification accuracy generated by the proposed method is improved. It also can maintain the robust performance on data sets even with various noises.

[1]  Xiangdong Wang,et al.  Multiscale local features learning based on BP neural network for rolling bearing intelligent fault diagnosis , 2020, Measurement.

[2]  Yang Song,et al.  Improving the Robustness of Deep Neural Networks via Stability Training , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Haidong Shao,et al.  A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders , 2018 .

[4]  Xianmin Zhang,et al.  Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions , 2020, Knowl. Based Syst..

[5]  Feng Jia,et al.  An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data , 2016, IEEE Transactions on Industrial Electronics.

[6]  Kun Jiang,et al.  A deep capsule neural network with stochastic delta rule for bearing fault diagnosis on raw vibration signals , 2019 .

[7]  Enrico Zio,et al.  Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.

[8]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[9]  Diego Cabrera,et al.  Bayesian approach and time series dimensionality reduction to LSTM-based model-building for fault diagnosis of a reciprocating compressor , 2020, Neurocomputing.

[10]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[11]  Peng Wang,et al.  Deep learning for fault diagnosis and prognosis in manufacturing systems , 2019, Computers in industry (Print).

[12]  Yuanqing Xia,et al.  A deep Boltzmann machine and multi-grained scanning forest ensemble collaborative method and its application to industrial fault diagnosis , 2018, Comput. Ind..

[13]  V. Rai,et al.  Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform , 2007 .

[14]  Yanxue Wang,et al.  Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system , 2015 .

[15]  Yaguo Lei,et al.  A review on empirical mode decomposition in fault diagnosis of rotating machinery , 2013 .

[16]  Ghulam Muhammad,et al.  Automatic Fruit Classification Using Deep Learning for Industrial Applications , 2019, IEEE Transactions on Industrial Informatics.

[17]  Shuilong He,et al.  A Novel Deep Learning Network via Multiscale Inner Product With Locally Connected Feature Extraction for Intelligent Fault Detection , 2019, IEEE Transactions on Industrial Informatics.

[18]  Jun Pan,et al.  A Deep Learning Network via Shunt-Wound Restricted Boltzmann Machines Using Raw Data for Fault Detection , 2020, IEEE Transactions on Instrumentation and Measurement.

[19]  Biao Wang,et al.  LiftingNet: A Novel Deep Learning Network With Layerwise Feature Learning From Noisy Mechanical Data for Fault Classification , 2018, IEEE Transactions on Industrial Electronics.

[20]  Hongli Gao,et al.  A new bearing fault diagnosis method based on modified convolutional neural networks , 2020, Chinese Journal of Aeronautics.

[21]  Diego Cabrera,et al.  Fault diagnosis in spur gears based on genetic algorithm and random forest , 2016 .

[22]  Ming J. Zuo,et al.  Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis: A review with examples , 2017 .

[23]  Yaguo Lei,et al.  Application of an improved kurtogram method for fault diagnosis of rolling element bearings , 2011 .

[24]  Steven X. Ding,et al.  Real-time fault diagnosis and fault-tolerant control , 2015, IEEE Transactions on Industrial Electronics.

[25]  Sherif Ishak,et al.  An extreme gradient boosting method for identifying the factors contributing to crash/near-crash events: a naturalistic driving study , 2019, Canadian Journal of Civil Engineering.

[26]  Yanyang Zi,et al.  Multiwavelet denoising with improved neighboring coefficients for application on rolling bearing fault diagnosis , 2011 .

[27]  Yanyang Zi,et al.  Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive , 2017 .

[28]  Siliang Lu,et al.  Bearing fault diagnosis of a permanent magnet synchronous motor via a fast and online order analysis method in an embedded system , 2017, Mechanical Systems and Signal Processing.

[29]  Jinglong Chen,et al.  Mono-component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data-driven adaptive Fourier spectrum segment , 2016 .

[30]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Jiawei Xiang,et al.  Improved deep convolution neural network (CNN) for the identification of defects in the centrifugal pump using acoustic images , 2020 .

[32]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[33]  Lin Lin,et al.  A novel gas turbine fault diagnosis method based on transfer learning with CNN , 2019, Measurement.

[34]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[35]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[36]  Liang Guo,et al.  A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines , 2018, Neurocomputing.

[37]  Bo-Suk Yang,et al.  Support vector machine in machine condition monitoring and fault diagnosis , 2007 .

[38]  Ahmad Ghasemloonia,et al.  Application and comparison of an ANN-based feature selection method and the genetic algorithm in gearbox fault diagnosis , 2011, Expert Syst. Appl..